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Both standalone language models (LMs) as well as LMs within downstream-tasksystems have been shown to generate statements which are factually untrue. Thisproblem is especially severe for low-resource languages, where training data isscarce and of worse quality than for high-resource languages. In this opinionpiece, we argue that LMs in their current state will never be fully trustworthyin critical settings and suggest a possible novel strategy to handle thisissue: by building LMs such that can cite their sources - i.e., point a user tothe parts of their training data that back up their outputs. We first discusswhich current NLP tasks would or would not benefit from such models. We thenhighlight the expected benefits such models would bring, e.g., quickverifiability of statements. We end by outlining the individual tasks thatwould need to be solved on the way to developing LMs with the ability to cite.We hope to start a discussion about the field's current approach to buildingLMs, especially for low-resource languages, and the role of the training datain explaining model generations.

Who Are All The Stochastic Parrots Imitating? They Should Tell Us!